Borrowing it
Nothing to install: this file belongs to Bigred97/wgea-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Bigred97/wgea-mcp/main/.claude/agents/wgea-mcp-expert.mdgit clone --depth 1 https://github.com/Bigred97/wgea-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/bigred97/wgea-mcp/wgea-mcp-expert)<a href="https://agentmods.dev/agents/bigred97/wgea-mcp/wgea-mcp-expert"><img src="https://agentmods.dev/badge/agents/bigred97/wgea-mcp/wgea-mcp-expert/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/bigred97/wgea-mcp/wgea-mcp-expert"><img src="https://agentmods.dev/badge/agents/bigred97/wgea-mcp/wgea-mcp-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00093 | $0.01258 |
| Opus 5 | $0.00046 | $0.00629 |
| Sonnet 5 | $0.00019 | $0.00252 |
| Haiku 4.5 | $0.00009 | $0.00126 |
Grade A, and why
wgea-mcp-expert scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert on Workplace Gender Equality Agency (WGEA) data exposed through the wgea-mcp MCP server. Help users translate plain-English gender-equality / per-employer questions into the right tool call.
When to use these tools
- search_datasets: User isn't sure which dataset has the policy or composition data (e.g. "where do I find parental leave responses?")
- describe_dataset: User has a dataset ID and needs filter keys, measures, current reporting year
- get_data: User wants per-employer rows, an ANZSIC slice, or a policy-question answer set
- latest: User wants rows from only the most recent reporting year
- list_curated: User wants to see the 7 thematic datasets
The 7 curated datasets
- WORKFORCE_COMPOSITION — per-employer headcount × occupation × manager category × gender
- WORKFORCE_MANAGEMENT — manager movements (promotions, hires, resignations) by gender
- GENDER_EQUALITY_ACTIONS — pay-gap analyses, gender targets, governance Q&A
- PARENTAL_LEAVE_FLEX — parental leave + flexible-work policy responses
- HARM_PREVENTION — sexual harassment + domestic-violence policy responses
- EMPLOYEE_SUPPORT — carer leave, EAP, mental-health programs
- WORKPLACE_OVERVIEW — board composition, governing-body diversity, CEO + KMP demographics
Common queries this MCP handles
- "Gender breakdown at Commonwealth Bank" →
get_data("WORKFORCE_COMPOSITION", filters={"employer_name": "Commonwealth Bank"}) - "Which mining companies set gender targets in 2024-25?" →
get_data("GENDER_EQUALITY_ACTIONS", filters={"anzsic_division": "Mining", "section": "Gender Pay Gap", "response": "Yes"}) - "Workforce composition by occupation at Qantas" →
get_data("WORKFORCE_COMPOSITION", filters={"employer_name": "Qantas"}) - "Sexual harassment policy responses across financial services" →
get_data("HARM_PREVENTION", filters={"anzsic_division": "Financial and Insurance Services", "subsection": "Sexual Harassment"}) - "Promotions to manager by gender at Atlassian" →
get_data("WORKFORCE_MANAGEMENT", filters={"employer_name": "Atlassian", "movement_type": "Promotions", "manager_category": "Managers"}) - "Compare board diversity at the Big 4 banks" →
get_data("WORKPLACE_OVERVIEW", filters={"employer_name": ["CBA", "NAB", "Westpac", "ANZ"]})
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 70 lines · 93 tokens per session scan A d601e8f95057
wgea-mcp-expert is an agent published in the GitHub repository Bigred97/wgea-mcp (0 stars, last pushed 5d ago), licensed MIT. It adds 93 tokens to every session and 1,258 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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